Abstract
This paper describes an object detection method based on sample consensus. Real time frame difference was employed to get the candidate foreground pixels. Then, a joint background model storing samples of visible and thermal videos was constructed through training to verify the initial foreground. So false positives produced by frame difference were reassigned to background. There were in total four channels (red, green, blue and thermal) in the proposed joint sample consensus background model. Our method performs object detection and fusion of both sensors’ information simultaneously, which reduces the complexity of our method. Experimental results illustrate that the presented detection method can achieve accurate detection results.
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References
Wang, H.Z., Suter, D.: A consensus-based method for tracking: Modelling background scenario and foreground appearance. Pattern Recogn. 40, 1091–1105 (2007)
Stauffer, C., Grimson, W., Eric, L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22, 747–757 (2000)
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90, 1151–1162 (2002)
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real Time Imaging 11, 172–185 (2005)
Barnich, O., Van Droogenbroeck, M.: ViBe: A universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20, 1709–1724 (2011)
Chiu, C.-C., Ku, M.-Y., Liang, L.-W.: A robust object segmentation system using a probability-based background extraction algorithm. IEEE Trans. Circuits Syst. Video Technol. 20, 518–528 (2010)
Davis, J.W., Sharma, V.: Background-subtraction in thermal imagery using contour saliency. International Journal of Computer Vision 71, 161–181 (2007)
Wang, J.-T., Chen, D.-B., Chen, H.-Y., Yang, J.-Y.: On pedestrian detection and tracking in infrared videos. Pattern Recogn. Lett. 33, 775–785 (2012)
Davis, J.W., Sharma, V.: Background-subtraction using contour-based fusion of thermal and visible imagery. Comput Vision Image Understanding 106, 162–182 (2007)
Kumar, P., Mittal, A., Kumar, P.: Addressing uncertainty in multi-modal fusion for improved object detection in dynamic environment. Inf. Fusion 11, 311–324 (2010)
Ulusoy, I., Yuruk, H.: New method for the fusion of complementary information from infrared and visual images for object detection. IET Image Proc. 5, 36–48 (2011)
Zivkovic, Z., Van Der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27, 773–780 (2006)
Brutzer, S., Höferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognit., pp. 1937–1944. IEEE Computer Society, Piscataway (2011)
Kasturi, R., et al.: Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol. IEEE Trans. Pattern Anal. Mach. Intell. 31, 319–336 (2009)
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Han, G., Cai, X., Wang, J. (2012). Consensus-Based Detection Method for Visible and Thermal Videos. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_16
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DOI: https://doi.org/10.1007/978-3-642-34041-3_16
Publisher Name: Springer, Berlin, Heidelberg
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